机器学习揭示了非抗生素药品在环境相关度下对抗生素耐药性基因型施加的选择压力
Shaojing Sun1, Yan Sun2, Jialu Geng3
1College of Energy and Environmental Engineering, Hebei Key Laboratory of Air Pollution Cause and Impact, Hebei Engineering Research Center of Sewage Treatment and Resource Utilization, Hebei University of Engineering, Handan, 056038, China; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China.
Journal of environmental management
|April 5, 2024
概括
废水中的非抗生素药物会影响抗生素耐药性基因 (ARG),但不会影响表型. 从废水处理厂 (WWTP) 中去除这些药物可以减少ARG的选择压力.
科学领域:
- 环境科学 环境科学
- 微生物学 微生物学
- 公共卫生 公共卫生
背景情况:
- 抗生素耐药性是一个日益增长的全球健康威胁.
- 在废水中越来越多地检测到非抗生素药品.
- 这些化合物可能会影响抗生素耐药性的发展和传播.
研究的目的:
- 调查常见非抗生素药品对废水中抗生素耐药性基因 (ARG) 和表型的影响.
- 确定特定药物度与ARG流行率之间的关系.
- 评估废水处理过程在去除这些药品和减轻抗性基因选择方面的有效性.
主要方法:
- 在101个废水样本中检测到五种非抗生素药物 (乙氨基,克洛纤维酸,卡巴马泽平,咖啡因,尼古丁).
- 五个ARG (sul1, sul2, tetG, tetM, tetW) 和intI1整合酶基因的量化.
- 废水处理过程的分析 (无氧氧气和生物气化过器).
- 机器学习的应用,将药物度与ARG亚型相关联.
- 抗生素耐药性表型的测量 (最小抑制度 - MIC).
主要成果:
- 非抗生素制药在环境相关度下显著影响了编码核糖体保护蛋白 (RPPs) 的四环素耐药基因.
- 没有观察到抗生素耐药性表型 (MIC) 的显著变化.
- 机器学习确定了尼古丁,乙氨基和咖啡因的特定度值,与增加RPP编码ARG患病率有关.
- 药品和ARG之间的相关性在尼古丁度低于500ng/L,乙氨基度低于100ng/L,咖啡因度低于1000ng/L时不存在,这些度高于检测到的废水度.
结论:
- 在环境相关的度下,非抗生素药品可以对特定的ARG施加选择性压力,特别是编码RPP的ARG.
- 废水处理过程可能无法完全去除这些药物,但它们在WWTP中的去除可以降低ARG的选择压力.
- 在废水处理中有针对性地去除非抗生素药品是缓解抗生素耐药性传播的潜在策略.
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